Bayesian Treed Multivariate Gaussian Process With Adaptive Design: Application to a Carbon Capture Unit
Bayesian Treed Multivariate Gaussian Process With Adaptive Design: Application to a Carbon Capture Unit
复制标题
具有自适应设计的贝叶斯树多元高斯过程：在碳捕获装置中的应用
作者：
B. Konomi;G. Karagiannis;A. Sarkar;Xin Sun;Guang Lin
计算机实验广泛应用于科学研究中，以研究和预测复杂系统的行为，这些系统通常具有由一组非平稳输出组成的响应。对于不同输入值的参数研究来说，高分辨率模拟的计算成本通常非常昂贵且不切实际。在本文中，我们开发了贝叶斯树多元高斯过程（BTMGP）作为贝叶斯树高斯过程（BTGP）的扩展，以对互协方差函数和多元输出的非平稳性进行建模。我们通过适当选择协方差函数和先验分布来降低马尔可夫链蒙特卡罗采样器的计算复杂性。基于BTMGP，我们开发了输入空间的顺序实验设计并构建了模拟器。我们在测试用例中演示了所提出的方法的使用，并将其与替代方法进行比较。我们还应用顺序采样技术和 BTMGP 对碳捕获装置的全尺寸再生器中的多相流进行建模。
Computer experiments are widely used in scientific research to study and predict the behavior of complex systems, which often have responses consisting of a set of nonstationary outputs. The computational cost of simulations at high resolution often is expensive and impractical for parametric studies at different input values. In this article, we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) to model the cross-covariance function and the nonstationarity of the multivariate output. We facilitate the computational complexity of the Markov chain Monte Carlo sampler by choosing appropriately the covariance function and prior distributions. Based on the BTMGP, we develop a sequential design of experiment for the input space and construct an emulator. We demonstrate the use of the proposed method in test cases and compare it with alternative approaches. We also apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit.
影响因子：
2.7
作者：
Giles, Michael B.
通讯作者：
Giles, Michael B.